While deep-learning-based image registration has received much interest recently, similarity metrics utilizing the compositional and data-driven advantages of neural networks remain under-explored. Some current works explore how to incorporate scale-space into learned registration models, but similarity metrics remain pixel-based [1]}, [2]}. Learned similarity metrics are proposed by [3]} and [4]}, but both approaches require ground truth registration maps, which are either synthetically generated or manually created by a medical expert. [5]} propose to learn annotated structures of interest as part of the registration model to aid alignment, but the method discards sub-regional and non-annotated structures.
